Digital Network Twins for Next-generation Wireless: Creation, Optimization, and Challenges
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Abstract (arXiv)
Digital network twins (DNTs), by representing a physical network using a virtual model, offer significant benefits such as streamlined network development, enhanced productivity, and cost reduction for next-generation (nextG) communication infrastructure. Existing works mainly describe the deployment of DNT technologies in various service sections.The full life cycle of DNTs for telecommunication has not yet been comprehensively studied, particularly in the aspects of fine-grained creation, real-time adaptation, resource-efficient deployment, and security protection. This article presents an in-depth overview of DNTs, exploring their concrete integration into networks and communication, covering the fundamental designs, the emergent applications, and critical challenges in multiple dimensions. We also include two detailed case studies to illustrate how DNTs can be applied in real-world scenarios such as wireless traffic forecasting and edge caching. Additionally, a forward-looking vision of the research opportunities in tackling the challenges of DNTs is provided, aiming to fully maximize the benefits of DNTs in nextG networks.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Oct 2024.
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Citing papers per year: 2025: 3; 2026: 5.
8 papers.
- 2025Optimizing Wireless Resource Management and Synchronization in Digital Twin Networkscited by 3
- 2025RAN Cortex: Memory-Augmented Intelligence for Context-Aware Decision-Making in AI-Native Networkscited by 1
- 2026Network Digital Untwinning: Towards Backward Optimization of Digital Twinscited by 1
- 2026CENTILE: A Telemetry Foundation Model Evaluated by the Decisions It Drivescited by 1
- 2025On Transferring, Merging, and Splitting Task-Oriented Network Digital Twinscited by 0
- 2026The Vienna 4G/5G Drive-Test Datasetcited by 0
- 2026Customized Generative AI Agent for Transportation Engineering Practice: A Development and Continued Pre-training Guidelinecited by 0
- 2026Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learningcited by 0
- 2026CENTILE: A Telemetry Foundation Model Evaluated by the Decisions It Drives3 Aug 2026
- 2026Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learning9 Jul 2026
- 2026Customized Generative AI Agent for Transportation Engineering Practice: A Development and Continued Pre-training Guideline27 Jun 2026
- 2026Network Digital Untwinning: Towards Backward Optimization of Digital Twins30 Apr 2026
- 2026The Vienna 4G/5G Drive-Test Dataset3 Mar 2026
- 2025On Transferring, Merging, and Splitting Task-Oriented Network Digital Twins2 Sep 2025
- 2025RAN Cortex: Memory-Augmented Intelligence for Context-Aware Decision-Making in AI-Native Networks6 May 2025
- 2025Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks7 Feb 2025